Project proposal: A modular reinforcement learning based automated theorem prover
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We propose to build a reinforcement learning prover of independent components: a deductive system (an environment), the proof state representation (how an agent sees the environment), and an agent training algorithm. To that purpose, we contribute an additional Vampire-based environment to package of OpenAI Gym environments for saturation provers. We demonstrate a prototype of using together with a popular reinforcement learning framework (Ray ). Finally, we discuss our plans for completing this work in progress to a competitive automated theorem prover.
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